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Article
Publication date: 30 March 2012

Ingrid Burbey and Thomas L. Martin

Locationprediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location

Abstract

Purpose

Locationprediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal locationprediction. Locationprediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, locationprediction turned personal, predicting individuals' next locations given their current locations.

Design/methodology/approach

This paper includes an overview of prediction techniques and reviews several locationprediction projects comparing the raw location data, feature extraction, choice of prediction algorithms and their results.

Findings

A new trend has emerged, that of employing additional context to improve or expand predictions. Incorporating temporal information enables locationpredictions farther out into the future. Appending place types or place names can improve predictions or develop prediction applications that could be used in any locale. Finally, the authors explore research into diverse types of context, such as people's personal contacts or health activities.

Originality/value

This overview provides a broad background for future research in prediction.

Details

International Journal of Pervasive Computing and Communications, vol. 8 no. 1
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 5 September 2016

Djamel Guessoum, Moeiz Miraoui and Chakib Tadj

The prediction of a context, especially of a user’s location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive…

Abstract

Purpose

The prediction of a context, especially of a user’s location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive adaptation for context-aware applications. This study/paper aims to propose a methodology that predicts a user’s location on the basis of a user’s mobility history.

Design/methodology/approach

Contextual information is used to find the points of interest that a user visits frequently and to determine the sequence of these visits with the aid of spatial clustering, temporal segmentation and speed filtering.

Findings

The proposed method was tested with a real data set using several supervised classification algorithms, which yielded very interesting results.

Originality/value

The method uses contextual information (current position, day of the week, time and speed) that can be acquired easily and accurately with the help of common sensors such as GPS.

Details

International Journal of Pervasive Computing and Communications, vol. 12 no. 3
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 28 June 2011

Teddy Mantoro, Akeem Olowolayemo, Sunday O. Olatunji, Media A. Ayu, Abu Osman and Tap

Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning…

Abstract

Purpose

Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single‐hidden‐layer neural networks.

Design/methodology/approach

Prediction approach to location determination based on historical data has attracted a lot of attention in recent studies, the reason being that it offers the convenience of using previously accumulated location data to subsequently determine locations using predictive algorithms. There have been various approaches to location positioning to further improve mobile user location determination accuracy. This work examines the location determination techniques by attempting to determine the location of mobile users by taking advantage of SS and SQ history data and modeling the locations using the ELM algorithm. The empirical results show that the proposed model based on the ELM algorithm noticeably outperforms k‐Nearest Neighbor approaches.

Findings

WiFi's SS contributes more in accuracy to the prediction of user location than WiFi's SQ. Moreover, the new framework based on ELM has been compared with the k‐Nearest Neighbor and the results have shown that the proposed model based on the extreme learning algorithm outperforms the k‐Nearest Neighbor approach.

Originality/value

A new computational intelligence modeling scheme, based on the ELM has been investigated, developed and implemented, as an efficient and more accurate predictive solution for determining position of mobile users based on location fingerprint data (SS and SQ).

Details

International Journal of Pervasive Computing and Communications, vol. 7 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 7 August 2017

Qiangbing Wang, Shutian Ma and Chengzhi Zhang

Based on user-generated content from a Chinese social media platform, this paper aims to investigate multiple methods of constructing user profiles and their effectiveness in…

Abstract

Purpose

Based on user-generated content from a Chinese social media platform, this paper aims to investigate multiple methods of constructing user profiles and their effectiveness in predicting their gender, age and geographic location.

Design/methodology/approach

This investigation collected 331,634 posts from 4,440 users of Sina Weibo. The data were divided into two parts, for training and testing . First, a vector space model and topic models were applied to construct user profiles. A classification model was then learned by a support vector machine according to the training data set. Finally, we used the classification model to predict users’ gender, age and geographic location in the testing data set.

Findings

The results revealed that in constructing user profiles, latent semantic analysis performed better on the task of predicting gender and age. By contrast, the method based on a traditional vector space model worked better in making predictions regarding the geographic location. In the process of applying a topic model to construct user profiles, the authors found that different prediction tasks should use different numbers of topics.

Originality/value

This study explores different user profile construction methods to predict Chinese social media network users’ gender, age and geographic location. The results of this paper will help to improve the quality of personal information gathered from social media platforms, and thereby improve personalized recommendation systems and personalized marketing.

Details

The Electronic Library, vol. 35 no. 4
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 31 December 2006

R. Chellappa Doss, A. Jennings and N. Shenoy

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the…

Abstract

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided Routing (P‐LAR) protocol. Simulation study and analytical results of P‐LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

Details

International Journal of Pervasive Computing and Communications, vol. 2 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 28 February 2023

Bin Wang, Huifeng Li, Le Tong, Qian Zhang, Sulei Zhu and Tao Yang

This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for…

Abstract

Purpose

This paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.

Design/methodology/approach

The authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.

Findings

The authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.

Originality/value

The authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.

Details

Data Technologies and Applications, vol. 57 no. 3
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 21 August 2017

Lauren S. Elkin, Kamil Topal and Gurkan Bebek

Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided…

Abstract

Purpose

Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks.

Design/methodology/approach

In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data.

Findings

The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets.

Originality/value

First, while extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

Details

Information Discovery and Delivery, vol. 45 no. 3
Type: Research Article
ISSN: 2398-6247

Keywords

Book part
Publication date: 22 June 2011

Randi Lunnan, Gabriel R.G. Benito and Sverre Tomassen

To what extent, why and where do multinational companies locate divisional headquarters (DHQs) abroad? This study of 30 of the largest listed companies in Norway over the…

Abstract

To what extent, why and where do multinational companies locate divisional headquarters (DHQs) abroad? This study of 30 of the largest listed companies in Norway over the 2000–2006 period shows that foreign-located DHQs have become relatively commonplace. A majority of DHQs located abroad are outcomes of foreign acquisitions, which suggests that obtaining legitimacy from local stakeholders such as customers, employees and investors is an important motivation. We also find that Norwegian companies emphasize efficiency and value creation in their location choices, as they tend to prefer other advanced and competitive countries as hosts for their DHQs. Distance from Norway is not significant. The off-shoring of strategic units such as DHQs is a phenomenon that occurs in advanced phases of companies' internationalization, beyond the point when familiarity and proximity still are key decision-making factors.

Details

Dynamics of Globalization: Location-Specific Advantages or Liabilities of Foreignness?
Type: Book
ISBN: 978-0-85724-991-3

Article
Publication date: 8 September 2020

Tipajin Thaipisutikul and Yi-Cheng Chen

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order…

Abstract

Purpose

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.

Design/methodology/approach

The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.

Findings

The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.

Originality/value

This study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

Details

Industrial Management & Data Systems, vol. 120 no. 10
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 7 October 2014

Prasad Ramchandra Baviskar and Vinod B. Tungikar

The purpose of this paper is to address the determination of crack location and depth of multiple transverse cracks by monitoring natural frequency and its prediction using…

Abstract

Purpose

The purpose of this paper is to address the determination of crack location and depth of multiple transverse cracks by monitoring natural frequency and its prediction using Artificial Neural Networks (ANN). An alternative to the existing NDTs is suggested.

Design/methodology/approach

Modal analysis is performed to extract the natural frequency. Analysis is performed for two cases of cracks. In first case, both cracks are perpendicular to axis. In second case, both cracks are inclined to vertical plane and also inclined with each other. Finite element method (FEM) is performed using ANSYSTM software which is theoretical basis. Experimentation is performed using Fast Fourier Transform (FFT) analyzer on simply supported stepped rotor shaft and cantilever circular beam with two cracks each.

Findings

The results of FEM and experimentation are validated and are in good agreement. The error in crack detection by FEM is in the range of 3-15 percent while 5-20 percent by experimentation. The database obtained by modal analysis is used to train the network of ANN which predicts crack characteristics. Validity of method is investigated by comparing the predictions of ANN with FEM and experimentation. The results are in good agreement with error of 7-16 percent between ANN and FEM while 9-21 percent between ANN and experimental analysis.

Originality/value

It envisages that the method is capable. It is an effective as well as an alternate method of fault detection in beam/rotating element to the existing methods.

Details

Multidiscipline Modeling in Materials and Structures, vol. 10 no. 3
Type: Research Article
ISSN: 1573-6105

Keywords

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